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  4. Mining Health Social Media with Sentiment Analysis
 
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Mining Health Social Media with Sentiment Analysis

Journal
Journal of Medical Systems
Journal Volume
40
Journal Issue
11
Date Issued
2016
Author(s)
Yang F.-C.
Lee A.J.T.  
Kuo S.-C.
DOI
10.1007/s10916-016-0604-4
URI
https://scholars.lib.ntu.edu.tw/handle/123456789/415108
URL
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84988649896&doi=10.1007%2fs10916-016-0604-4&partnerID=40&md5=8525b27d62ac9ce19d12870801801fd5
Abstract
With the rapid development of the Internet, more and more users utilize health communities (known as forums) to find health-related information, share their medical stories and experiences, or interact with other people in the communities. In this paper, we propose a framework to analyze the user-generated contents in a health community. The proposed framework contains three phases. First, we extract medical terms, including conditions, symptoms, treatments, effectiveness and side effects to form a virtual document for each question in the community. Next, we modify Latent Dirichlet Allocation (LDA) by adding a weighted scheme, called conLDA, to cluster virtual documents with similar medical term distributions into a conditional topic (C-topic). Finally, we analyze the clustered C-topics by sentiment polarities, and physiological and psychological sentiment. The experiment results show that conLDA outperforms the original LDA, and can cluster relevant medical terms and relevant questions together. The C-topics clustered by conLDA are more thematic than those clustered by the original LDA. The results of sentiment analysis may provide a quick reference and valuable insights for patients, caregivers and doctors. ? 2016, Springer Science+Business Media New York.
Subjects
Health social media
Latent Dirichlet Allocation
Sentiment analysis
SDGs

[SDGs]SDG3

Other Subjects
caregiver; experimental model; extract; human; human experiment; mining; social media; symptom; consumer health information; data mining; procedures; social media; statistics and numerical data; Consumer Health Information; Data Mining; Humans; Social Media
Type
journal article

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To permanently archive and promote researcher profiles and scholarly works, Library integrates the services of “NTU Repository” with “Academic Hub” to form NTU Scholars.

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醫學圖書館學科館員 (Medical Library)
社會科學院辜振甫紀念圖書館學科館員 (Social Sciences Library)

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